Advancement in genetic variants conferring obesity susceptibility from genome-wide association studies

Abstract

Obesity prevalence has increased in recent years. Lifestyle change fuels obesity, but genetic factors cause more than 50% of average variations in obesity. The advent of genome-wide association studies (GWAS) has hastened the progress of polygenic obesity research. As of this writing, more than 73 obesity susceptibility loci have been identified in ethnic groups through GWAS. The identified loci explain only 2% to 4% of obesity heritability, thereby indicating that a large proportion of loci remain undiscovered. Thus, the next step is to identify and confirm novel loci, which may exhibit smaller effects and lower allele frequencies than established loci. However, achieving these tasks has been difficult for researchers. GWAS help researchers discover the causal loci. Moreover, numerous biological studies have been performed on the polygenic effects on obesity, such as studies on fat mass- and obesity-associated gene (FTO), but the role of these polygenic effects in the mechanism of obesity remains unclear. Thus, obesity-causing variations should be identified, and insights into the biology of polygenic effects on obesity are needed.

Keywords

obesity / genetics / genome-wide association studies / body mass index / fat mass- and obesity-associated gene

Introduction

Obesity is a serious international health problem that increases the risk of type 2 diabetes, hyperlipidemia, cardiovascular diseases, and several types of cancer. Obesity is measured clinically with the surrogate measure of body mass index (BMI), calculated as weight divided by the square of height. According to the estimation of the International Association for the Study of Obesity and International Obesity Task Force in 2010, approximately 1 billion adults are overweight (BMI: 25 kg/m² to 29.9 kg/m²), and 475 million adults are obese (BMI:≥ 30 kg/m²) globally [ 1]. The given BMI values indicate a higher percentage of body fat and metabolic disease risk for Asian populations than European populations. Asian individuals with a BMI of≥28 kg/m² are considered obese. The prevalence of obesity (BMI:≥28 kg/m²) in China increased from 3.6% in 1992 to 7.1% in 2002 and reached 12.2% in 2007 to 2008 [ 2].

Obesity incidence is affected by both environmental and genetic factors. A rapid change in lifestyle fuels obesity. However, high heritability of obesity was estimated from twin studies (47% to 90%), and moderate-to-high heritability was estimated from family and adoption studies (24% to 81% and 20% to 60%, respectively) [ 3, 4].

Genome-wide association studies (GWAS) have contributed to the progress of research in identifying the genetic variations of obesity. Most of these studies were performed among European-ancestry populations, but a growing number of studies are being performed in non-European ancestry populations. As of this writing, at least 73 loci affecting obesity-related traits, including 59 and 14 loci from European and non-European populations, respectively, have been identified worldwide. In this review, we summarized the recent advances in polygenic obesity genetics research using data from GWAS in different populations. We used the susceptibility loci in the fat mass- and obesity-associated gene (FTO) as an example to determine the biological pathways underlying the associations between FTO and obesity.

Advances in GWAS on obesity

GWAS are currently and commonly used to identify genetic variants associated with complex diseases and traits of interest among unrelated subjects. GWAS, which include a two-stage design, screen the whole genome at high resolution level. The associations from the two stages are meta-analyzed and adjusted for multiple testing. Thus, GWAS generally only consider associations that reach a value of P<5.0×10^-8. Using data from GWAS has significantly contributed to the progress of research on complex diseases and traits of interest. However, this strategy has limitations, as follows: insufficient sample size, lack of proper correction for multiple testing, genotype reliability in quality control, detecting only common SNPs (minor allele frequency>1%), and difficulty in distinguishing causal SNPs among related SNPs.

Progress in the search for genetic variants that contribute to obesity susceptibility was achieved with the advent of GWAS. More than 73 loci have reached genome-wide significance (P<5.0×10^-8). Among these 73 loci, 42 were identified for BMI, 3 for body fat percentage, 26 for waist circumference (WC) or waist-hip ratio (WHR), and 2 for subcutaneous fat area (SFA) and visceral fat area (VFA) (Tables 1 and 2).

Genetic loci for BMI

BMI is a highly heritable trait that is easy and inexpensive to measure. Because of its clear definition, BMI is available and feasible for large-scale discovery and replication of GWAS. Most GWAS were conducted predominantly in European populations, but a certain proportion of GWAS in non-European populations was also used in polygenic obesity research.

European populations

The first convincing genome-wide association study on obesity-related traits was published by Fraying et al. [ 5] in Science. This study was conducted in 1924 UK type 2 diabetes patients and 2938 UK controls from the Welcome Trust Case Control Consortium. A total of 490€032 autosomal single-nucleotide polymorphisms (SNPs) were obtained. An SNP rs9939609 in the FTO was identified and replicated in 13 cohorts with 38 759 participants. The risk allele A in the FTO was strongly associated with increased BMI with a median per-allele change of approximately 0.36 kg/m² (in the range 0.34 kg/m² to 0.46 kg/m²) among adults.

The second GWAS-identified BMI locus with statistical significance was reported by the Genetic Investigation of Anthropometric Traits (GIANT) consortium in 2008 [ 6]. A variant rs17782313 in the melanocortin-4 receptor gene (MC4R) was identified with BMI and replicated among 75 981 European subjects. By combining the two-stage data of genotyped adults, the association of each copy of the rs17782313 C allele with an increased BMI of 0.22 kg/m² was determined. To validate the effects of two loci and to search for novel loci, three meta-analyses of GWAS were successively performed among European individuals, and the total number of novel BMI-related loci reached 32. Three meta-analyses of GWAS, which were conducted by Willer [ 7], Thorleifsson [ 8], and Speliotes [ 9], confirmed the occurrence of previously reported susceptibility loci and identified 6, 8, and 20 novel BMI-related loci, respectively. Subsequent to their discovery, these novel loci have been replicated in various individuals of white European descent [ 7, 10], including children and adolescents [ 11, 12].

Overall, 1.45% of the inter-individual variation in BMI was explained by 32 confirmed BMI loci [9]. In addition, the locus in FTO showed the largest proportion of variance (0.34%) [ 9]. FTO is the most easily established locus because it has the largest effect size among loci and is a high risk frequency allele in European populations. Other loci that may exhibit smaller effects and lower allele frequencies than FTO need to be identified and confirmed. However, these tasks have been difficult for researchers.

Non-European populations

GWAS, which were predominantly performed in samples of European ancestry, have identified numerous BMI-related loci. However, whether these loci could be generalized to other ethnic groups for genetic distinction remains unknown. Hence, several GWAS involving Asian, African, and other ethnic groups have been performed to validate the relationship between susceptibility loci and BMI in various ethnic groups.

In 2011, a meta-analysis of existing data from 96 551 East and South Asian populations was conducted by Li et al. [ 13] to test the associations of the FTO locus with obesity and type 2 diabetes. The FTO was correlated with increased risk of obesity (P = 9.0×10^-19), and the FTO-rs9939609 minor allele increased BMI by 0.26 kg/m² per allele (P = 2.8×10^-17).

In 2012, two important large-scale GWAS involving Asian populations were performed and published in Nature Genetics at around the same time. These GWAS were breakthrough studies on Asians. The two-stage GWAS by Okada et al. [ 14] were conducted among 26 620 Japanese subjects in the discovery stage, and the findings were replicated in an additional sample of 7900 Japanese individuals. Okada et al. found two novel BMI-related loci in CDKAL1 and KLF9 (P = 1.4×10^-11 and P = 1.3×10^-9, respectively) and several other loci (SEC16B, BDNF, FTO, MC4R, and GIPR loci, P<5.0×10^-8). Another study by Wen et al. [ 15] involved 27 715 East Asians, and this study was followed by in silico and de novo replications involving 37 691 and 17 642 additional East Asian individuals, respectively. Seven previously reported loci in or near FTO, SEC16B, MC4R, GIPR-QPCTL, ADCY3-DNAJC27, BDNF, and MAP2K5 were confirmed among these Asian populations. Moreover, three novel BMI-related loci in or near CDKAL1, PCSK1, and GP2 were identified (P = 1.02×10^-8 to 2.00×10^-11); the CDKAL1 locus was also reported by Okada et al. [14]. The two studies jointly reported four novel loci harboring CDKAL1, PCSK1, GP2, and KLF9 among Asians.

In 2014, Wen et al. [ 16] conducted a new round of meta-analyses to test the associations between BMI and 2.5 million genotyped or imputed SNPs in a population of 86 757 Asians and replicated the top significant SNPs in an additional sample of 7488 to 47 352 Asians. Wen et al. successively discovered four novel BMI-associated loci near KCNQ1, ALDH2/MYL2, ITIH4, and NT5C2 (P ranging from 3.83×10^-8 to 9.29×10^-13) and 8 established loci near FTO, BDNF, SEC16B, MC4R, TMEM18, GIPR/QPCTL, ADCY3/RBJ, and GNPDA2. To test whether these novel loci were specific to Asians or generalized to other ethnic groups, all three studies further evaluated the associations of BMI with these eight loci in a European population by using the data of 123 865 subjects from the GIANT consortium [ 9]. Six loci in CDKAL1, PCSK1, GP2, KLF9, ITIH4, and NT5C2 showed the same directional effects of the alleles on BMI in the European populations from the GIANT consortium compared with those in Asian populations, thereby suggesting that these novel loci were not specific to Asians. The SNPs in the ALDH2 and MYL2 were monomorphic in HapMap European-ancestry data. Thus, data for analysis were unavailable. The transferability of these eight novel loci across other ethnic populations is still a problem that requires further study. The samples of three GWAS in Asian populations were smaller than those in the GIANT study, but were sufficient to uncover the novel loci for better statistical power, thereby indicating that GWAS should be conducted across diverse populations.

Nearly 50% of African-American adults were classified as obese, whereas only 35% of non-Hispanic whites were obese [ 17]. With the ethnic disparities in the prevalence of obesity in the United States, genetic variants that are important or specific to the African-American population need to be found. Thus, investigators have sought to conduct GWAS on obesity in African populations.

In 2012, two-stage BMI GWAS in African American populations, involving 746 626 SNPs in 816 non-diabetic and 899 diabetic nephropathy subjects, were performed [ 18]. After adjustment for age, gender, disease status, and population structure, six high-scoring SNPs that showed nominal association with BMI were further replicated in 3274 additional subjects in four cohorts. The result of meta-analysis revealed four putative BMI-related loci at PP13439-TMEM212, CDH12, MFAP3-GALNT10, and FER1L4 with suggestive associations (P ranging from 2.4×10^-6 to 5×10^-5). Monda et al. performed the latter GWAS meta-analysis of BMI involving 39 144 individuals of African ancestry [ 19]. Monda et al. replicated the most significant associations in an additional group of 32 268 individuals. Two novel loci and one highly suggestive locus affecting BMI were identified along with five established BMI loci at FTO, MC4R, GNPDA2, ADCY3, and SEC16B. The new loci were located in GALNT10 and MIR148A-NFE2L3 (P = 3.4×10^-11 and P = 1.2×10^-10, respectively), and the third one was located in KLHL32 (P = 6.9×10^-8). The carriers of risk alleles showed increased BMI by 0.031 kg/m² to 0.073 kg/m² per allele. Further replication and meta-analysis in an African population and in other populations are required to improve the understanding of the role of these loci in different ethnic populations.

A total of 42 susceptibility loci were identified for BMI via global GWAS. Previously identified loci, such as FTO and MC4R, showed the highest number of variations and were also widely found across the different ethnic groups. Understanding the similarities and differences in genetic susceptibility across diverse ethnic groups might eventually contribute to the fine-mapping of the causal genes and might lead to a functional follow-up.

Genetic loci for body fat percentage

As the diagnostic criterion for overweight and obesity, BMI is a good proxy for measuring obesity. However, BMI fails to distinguish between lean mass and fat mass.

Body fat percentage, which is more accurate than BMI, can be used to solve this problem to some extent. To uncover the specific loci accounting for this phenotype, Kilpelainen et al. [ 20] conducted a meta-analysis among 36 626 individuals of white European and Indian-Asian descent and among 39 576 European individuals. Kilpelainen et al. identified two loci near IRS1 and SPRY2, and these loci were correlated with body fat percentage (P = 4×10^-11 and P = 3×10^-8, respectively). Kilpelainen et al. confirmed the locus in FTO (P = 3×10^-26) with effect sizes ranging from 0.15% to 0.33% per risk allele. Moreover, genetic variation near IRS1 was intriguingly associated with reduced adiposity and an impaired metabolic profile. This locus increases the risk of type 2 diabetes via its effect on insulin resistance. GWAS on body fat percentage across diverse populations are limited. More large-scale GWAS related to body fat percentage are required.

Genetic loci for WC and WHR

Central obesity is an important risk factor for metabolic and cardiovascular diseases [ 21– 23]. The polygenic background of central obesity appears to be crucial in understanding the complex etiology of obesity-related diseases. As described above, numerous BMI-related loci have been identified through GWAS, but these loci were not specifically attributed to inter-individual variation in central obesity and fat distribution. WC and WHR are generally regarded as the markers of central obesity and fat distribution. Therefore, efforts to identify loci that affect WC and WHR could provide new insights into central obesity.

European populations

Two important GWAS identified variations in or near FTO, MC4R, NRXN3, TFAP2B, and MSRA, and such variations were associated with WC in European populations. One study involving 31 373 European individuals was conducted by Heard-Costa et al. [ 24]. Results of this study confirmed the effect of FTO and MC4R on WC and revealed a WC-associated novel locus in NRXN3. The association with NRXN3 was further confirmed in 38 641 individuals from the GIANT consortium (P = 5.3×10^-8). The other GWAS-meta analysis was conducted by Lindgren et al. [ 25] among 38 580 individuals, followed by a large-scale replication involving up to 70 689 individuals. The results showed two novel loci harboring TFAP2B and MSRA, which were strongly associated with WC (P = 1.9×10^-11 and P = 8.9×10^-9; conferring effect sizes of 0.49 and 0.43 cm, respectively) and another locus near LYPLAL1, which showed gender-specific relationship with WHR (in women P = 2.6×10^-8; with an effect size of 0.0014).

Measures of central and overall obesity are correlated with each other (BMI has r² = 0.9 with WC and r² = 0.6 with WHR) [ 25]. Thus, the association with WC remained insignificant or only nominally significant after additionally adjusting for BMI in these two studies. These loci were more likely to be involved in regulating overall obesity than WC.

The estimates of anthropometric measures of heritability of central adiposity remained high (60% for WC and 45% for WHR) after correcting for BMI, thereby suggesting that signals affecting fat distribution may be independent of the predominantly neuronal mechanisms that control the overall energy balance [ 26]. Focusing on this issue, Heid et al. [ 27] conducted a meta-analysis of 32 GWAS for WHR adjusted for BMI (77 167 participants) and followed up 16 loci in 29 additional studies (113 636 participants) in 2010. Heid et al. uncovered 13 novel loci, which showed effect sizes ranging from 0.022 to 0.042 (P ranging from 1.9×10^-9 to 1.8×10^-40) for WHR adjusted for BMI. Seven of the 13 loci had stronger effect on WHR among women than among men (P = 1.2×10^-13vs. 1.9×10^-3) after gene-by-sex interactions analysis, which was in agreement with Lindgren’s result on LYPLAL1.

In contrast to subsequent stratified analyses by sex, the latest systematic search for obesity-related susceptibility loci with sexually dimorphism through GWAS was conducted by Randall et al. [ 28]. The study separately investigated men and women from a population of 133 723 Europeans (60 586 men and 73 137 women) in the discovery stage and from a population of 137 052 Europeans (62 395 men and 74 657 women) in the replication stage. Randall et al. found 3 novel women-specific loci associated with WC (near MAP3K1) or WHR (near HSD17B4 and PPARG) and 4 established loci for WC or WHR among women after adjusting for BMI (P<5×10^-8). The results of this study highlighted the importance of considering sex effects on WC or WHR.

Non-European populations

The locus near MC4R was initially found to be correlated with BMI among European individuals in 2008 [ 6]. Around the same time, Chambers et al. [ 29] conducted a GWAS meta-analysis in a population of 2684 Indians, with further replication in 11 955 individuals of Indian Asian or European ancestry. Chambers et al. identified the association of rs12970134 near MC4R with WC (P = 1.7×10^-9). Homozygotes for the risk allele have increased WC by approximately 2 cm. Moreover, large-scale GWAS on Asian populations (8842 and 7861 samples in stages 1 and 2, respectively) also uncovered a novel locus affecting WHR. This locus was rs2074356 near ALDH2 (P = 7.8×10^-12, effect size of 0.007) and was in moderate linkage disequilibrium (LD r² = 0.58 in CHB+ JPT) with the functional Glu504Lys variant in ALDH2 [ 30]. Unfortunately, whether the effect of this novel locus on WHR is independent of overall obesity without adjustment for BMI remains unclear. The results of GWAS meta-analysis of WHR in up to 23 564 Africans and in a follow-up replication sample of 10 027 Africans revealed two novel suggestive loci near LHX2 and RREB1 for WC and WHR after controlling BMI (single genomic control, P = 2.24×10^-8 and 2.48×10^-8, respectively) [ 31]. Further studies with bigger sample sizes are required to validate the correlation between variations and WC or WHR after adjustment for BMI.

Overall, the results of studies in both European and non-European populations demonstrated that the loci involved in central obesity and fat distribution were distinct from those affecting BMI. Thus, different processes and mechanisms between these loci are indicated. Furthermore, most of the loci for WC or WHR showed sexual dimorphism, but the underlying mechanisms are unclear. Large-scale GWAS on populations of different ancestries can help identify other loci and pinpoint the causal genes for functional and mechanistic studies.

Genetic loci for SFA and VFA

WC and WHR are good surrogates for determining central obesity, but they are limited by their inability to distinguish subcutaneous adipose tissue (SAT) from visceral adipose tissue (VAT). Additionally, VAT was apparently correlated with cardiometabolic diseases caused by central obesity [ 32, 33] and could lead to alteration in the plasma levels of adipocytokines, thereby resulting in the development of cardiometabolic diseases [ 34]. SFA and VFA can be measured precisely and directly via magnetic resonance imaging (MRI) and computed tomography (CT), both of which may accurately reveal the genetic effects of these loci on central obesity and fat distribution. The application of SFA and VFA appears to be beneficial for the validation of the role of WC or WHR-related loci and for fine mapping. In 2012, Fox et al. [ 35] conducted two-stage GWAS on central obesity (quantified by CT) among 5560 women and 4997 men of European descent. Fox et al. conducted follow-up GWAS involving 70 877 individuals from the GIANT consortium. They identified a locus rs11118316 near LYPLAL1 for VAT/SAT ratio (P = 3.1×10^-9) and a novel locus rs1659258 near THNSL2 with sexual dimorphism (only in women P = 1.6×10^-8), thereby indicating the use of highly precise measurements of central obesity.

SFA and VFA (quantified by CT or MRI) are accurate and specific for central obesity, but are not ideal phenotypes. SFA and VFA may not allow the determination of causal variations in achievable sample sizes because of various issues, including high costs. However, their potential values should be emphasized, and GWAS on diverse populations are required.

Fine-mapping and mechanism studies of obesity-related loci

A large number of obesity-related loci have been identified through GWAS. However, the GWAS strategy is limited to sorting out which loci within these regions might be obesity-causing genetic variations. The kind of role these obesity-related loci could play in the obesity etiology must be determined. Nearly all previous studies have shown that FTO was associated with obesity-related traits, such as BMI [ 5, 6, 13], body fat percentage [ 20], WC, and WHR [ 13, 24, 29]. In this Section, we discuss the determination of biological pathways underlying the associations between FTO and obesity.

Fine-mapping

FTO was the first obesity susceptibility gene discovered through GWAS; previously FTO was an unknown gene with an unknown function [ 5]. Several SNPs, e.g., rs9939609 [ 5], rs9930506 [ 10], rs17817449 [ 36], and rs12149832 [ 37], in FTO were shown to be associated with obesity-related traits. The associations were widely replicated across diverse ancestries. However, the associations between FTO SNPs and phenotypes merely allowed the use of an approach close to the causal variations. None of the features suggested that any of these SNPs represented the functional variants, and we could not be sure which of nearby loci might be causal variations. Linkage disequilibrium (LD), a measure of association between two alleles, is of great value for fine-mapping LD disparities in ethnic groups. For example, in European populations, the cluster that includes all BMI-related SNPs in FTO identified through GWAS covered a ~47 kb region in the first intron of FTO, and this region most likely contained the predisposing variants [ 5]. The cluster of BMI-associated FTO SNPs in Asian populations was similar to that in European populations [ 16], but was different from that in African populations [ 19]. The correlation between SNPs in the first intron of FTO in African populations was substantially weaker than that in European or East Asian populations. Thus, the most significant FTO SNP (rs17817964) in African populations represented a cluster of few SNPs across a small region, which might narrow down the region harboring the causal FTO variants. Therefore, genetic association studies on individuals with different genetic background should be conducted. These associations will be investigated further in experimental studies.

Mechanistic studies on FTO

Function of FTO

Some researchers focused on the association of FTO with obesity since its discovery in 2007. Obesity results from energy imbalance, which is modulated by food intake and energy expenditure. To understand the function and potential mechanisms of FTO, several studies were conducted to test whether SNPs in FTO are associated with food intake and physical activity. Several studies showed associations of FTO SNPs, which conferred a predisposition to obesity with food intake; such SNPs may have a role in the control of satiety, food intake, and food choice [ 38– 40]. These SNPs may be associated with hyperphagic phenotype or a preference for energy-dense foods. GWAS on macronutrient intake in a sample population of more than 70 000 European individuals showed that the BMI-related loci of FTO is associated with increased protein intake after adjusting for effect on BMI (P = 3×10^-7) [ 41]. The study reported that individuals homozygous for the FTO rs9939609 risk A had dysregulated circulating levels of the orexigenic hormone acyl-ghrelin and attenuated postprandial appetite reduction. Furthermore, results obtained through functional MRI have indicated that FTO genotype modulated the neural responses to food images in homeostatic and brain reward regions [ 42]. A recent study by Llewellyn reported that satiety responsiveness is an intermediate behavioral phenotype that is associated with the genetic predisposition of 28 established loci to obesity among children [ 43]. Compared with the productive exploration of food intake, SNPs in FTO were not associated with the levels of physical activity, and physical activity failed to mediate the association between FTO and obesity susceptibility, as expected [ 44, 45]. Nonetheless, results of previous studies suggested that physical activity attenuated the influence of variants on obesity risk and can improve body weight regulation among obese adults [ 46– 48].

Epidemiology studies have focused on the biological function of FTO. FTO is located in chromosome 16 in humans and in chromosome 8 in mouse. FTO encodes the protein that belongs to 2-oxyglutarate and Fe(II)-dependent demethylase family; this protein has a potential role in nucleic acid repair or modification [ 49]. The expression of this protein is regulated by fasting and feeding in mice [ 50], thereby indicating its functional involvement in energy homeostasis. FTO protein is widely expressed in fetal and adult tissue, and the highest expression was found in the arcuate nucleus of the hypothalamus [ 49], which is known for its key role in appetite and energy balance. The overexpression or knockdown of the FTO in mice can provide important insights into the function of FTO. Mice expressing the FTO knockdown had apparent hyperphagia (Fto^–/–) and were resistant to high-fat diet-induced obesity (Fto^{+ /–}) [ 51]. FTO overexpression increased food intake and resulted in obesity in mice. Moreover, FTO overexpression resulted in a dose-dependent increase in body and fat mass, regardless of the type of diet provided to the mice (standard or high-fat diet) [ 52]. These studies indicate the association between FTO and energy homeostasis mediated by food intake, and such findings are consistent with the reported effects of FTO alleles on eating behavior in humans [ 38]. However, a recent study revealed that FTO knockout mice showed reductions in adipose tissue and lean body mass as a consequence of sympathetic system activation and increased energy expenditure [ 51]. Both food intake and energy expenditure are important in maintaining energy balance. The way by which the FTO product affects energy balance remains unknown and requires further investigation.

Molecular mechanism studies

Susceptibility SNPs are located in the first intron of FTO. Thus, they might subtly upregulate or downregulate FTO expression and further affect body mass and composition phenotypes. Researchers have attempted to study the underlying biological reasons for this phenomenon. For example, results of some studies have shown that FTO mRNA and protein levels were dramatically downregulated by amino acid deprivation in mouse and human cell lines [ 53], which might lead to a growth retardation phenotype with FTO deficiency. This result was in line with the fact that FTO is nutritionally regulated [ 49]. Obesity susceptibility SNPs in FTO were located near the transcriptional start site of RPGRIP1L, the human ortholog of mouse Ftm, which codes for the opposite DNA strand. FTO and RPGRIP1L are co-regulated, and FTO functions as a transcriptional co-activator of CCAAT/enhancer binding proteins. Thus, the association between FTO and body weight regulation might be mediated via expression changes in both FTO and RPGRIP1L [ 54]. This hypothesis has not been proven yet. A direct link between IRX3 expression and regulation of body mass and composition was demonstrated by the 25% to 30% reduction in body weight in Irx3-deficient mice; weight reduction was primarily through the loss of fat mass and the increase in basal metabolic rate with the browning of white adipose tissue [ 55]. This study was based on the hypothesis that the obesity-related SNPs within FTO are functionally connected with the regulation of IRX3 expression, and that IRX3 is an important determinant of body mass and composition. The precise molecular mechanisms by which IRX3 regulates metabolic parameters require further investigated.

Furthermore, the integration of epidemiology, animal, and molecular mechanism studies will likely elucidate the pathways underlying the effects of these variants on obesity risk.

Research directions

Despite the remarkable progress made in the field of polygenic obesity through GWAS, several issues remain unresolved.

First, the heritability of obesity estimated from family, adoption, and twin studies is>50% on average. Hence, the established loci explain only a fraction of the heritability, i.e., 2% to 4% of the variability is attributed to genetics [ 9]. A novel method called genome-wide complex trait analysis (GCTA) explains the missing heritability of BMI. Moreover, 16.5% of BMI variations in adults [ 56] and 30% of BMI variations in children [ 57] were estimated with GCTA. Therefore, a high number of loci remain unidentified, and established loci may harbor low-frequency variants. On the one hand, we should add more SNPs with small effect sizes to capture genetic variations. On the other hand, refined GWAS are required to discover novel obesity-related loci. Such GWAS include those that involve a big sample size or imputation of genotypes, highly precise traits or phenotypes, populations of non-European descent, and exome or whole genome sequencing.

Second, interactions between gene and environment may be confounding factors that obscure the actual association of the susceptibility loci with obesity. Min [ 58] showed that the heritability of BMI is sensitive to age (<20 years old), high BMI, gross domestic product, and rapid economic growth. Hence, gene-environment interactions need to be explored. Researchers should target samples with these characteristics to uncover novel loci.

Third, genetic risk scores (GRSs) can be an efficient and effective means of constructing genome-wide risk measurements from GWAS findings for etiological and treatment research. Llewellyn [ 64] reported that associations between the GRS based on 28 known obesity SNPs and obesity were significantly mediated by satiety responsiveness. Zhu [ 65] recently found that GRSs of obesity confer the risk of type 2 diabetes among Han Chinese individuals, regardless of BMI and partly through impaired β cell function. In addition, Qi’s report [ 67] indicated the interaction of fried food consumption with genetic background in relation to obesity, as calculated by a genetic risk score based on 32 BMI-associated variants on BMI. The approach using a genetic risk score pools information from multiple SNPs rather than from a single locus, thereby strengthening the power of studies. However, a systematic and replicable approach to selecting SNPs is the key to obtain appropriate GRSs. The study suggested caution in using GRS derived from GWAS on obesity among European populations or GWAS on obesity prediction among non-European populations [ 59]. Moreover, results of studies show that genetic predisposition to obesity varies across sex and life course. As more GWAS become available, obtaining sex-specific obesity GRS or evaluating obesity GRS in longitudinal cohorts becomes inevitable.

GWAS usually focus on the most significant individual variants without considering the potential functional interactions of the gene set. However, the alternative approach of pathway-based GWA analysis could identify additional variants and biological pathways that predispose to obesity. For example, Simonson [ 60] performed a whole-genome pathway analysis of BMI in 123 865 subjects from the GIANT Consortium, and subsequently used a replication sample comprising 8632 subjects. After multiple testing, the results indicated six pathways with significant enrichment for associations with BMI. Pathway analysis could detect significantly enriched pathways that do not contain specific candidate genes or significant SNPs.

Conclusions

With the advent of GWAS, at least 73 susceptibility loci associated with obesity or obesity-related traits have been uncovered at a stringent level of significance as of this writing. Each variation contributed a subtle effect. Research on the association between susceptibility loci and obesity showed huge progress. However, pinpointing obesity-causing variations and obtaining insight into the biology of polygenic effects on obesity are required to predict the risk of obesity and delivering interventions to those in need.

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